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Intensive heuristic and metaheuristic research efforts have focused on the Pareto front optimization of discrete time-cost trade-off problem (DTCTP). However, very little success has been achieved in solving the problem for medium and large-scale projects. This paper presents a new particle swarm optimization method to achieve an advancement in the Pareto front optimization of medium and large-scale construction projects. The proposed Pareto front particle swarm optimizer (PFPSO) is based on a multiobjective optimization environment with novel particle representation, initialization, and position-updating principles that are specifically designed for simultaneous time-cost optimization of large-scale projects. PFPSO brings several benefits for the discrete time-cost optimization, such as an adequate representation of the discrete search space, fast convergence properties, and improved Pareto front optimization capabilities. The computational experiment results reveal that the new particle swarm optimization method outperforms the state-of-the-art methods, both in terms of the number of Pareto front solutions and computation time, especially for medium and large-scale problems. A large number of nondominated solutions are achieved within seconds for the first time, for a problem including 720 activities. The proposed Pareto front particle swarm optimizer provides a fast and effective method for optimal scheduling of construction projects. [ABSTRACT FROM AUTHOR]

This paper presents an innovative model for scheduling, resource planning, and cost optimization of large construction and/or maintenance programs that involve multiple distributed sites. The proposed distributed scheduling model (DSM) employs an information system to store data related to various work sites, activities’ optional construction methods, and available resources. The DSM also incorporates a scheduling algorithm that is resource focused, and maintains crew work continuity under any sequence of distributed sites. To minimize cost and meet project time and resource constraints, the DSM uses genetic algorithms to determine the optimum set of construction methods and the optimum routing order among sites. Details of the DSM formulation are presented in this paper and the benefits to municipalities and contractors are outlined. [ABSTRACT FROM AUTHOR]

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme. [ABSTRACT FROM AUTHOR]

The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions. [ABSTRACT FROM AUTHOR]

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective optimization model that provides new and unique capabilities including generating and evaluating optimal/near-optimal construction resource utilization and scheduling plans that simultaneously minimize the time and maximize the profit of construction projects. The computations in the present model are organized in three major modules: (1) a scheduling module that develops practical schedules for construction projects; (2) a profit module that computes the project profit; and (3) a multiobjective module that searches for and identifies optimal/near optimal trade-offs between project time and profit. A large-scale construction project is analyzed to illustrate the use of the model and to demonstrate its capabilities in generating and visualizing optimal trade-offs between construction time and profit. [ABSTRACT FROM AUTHOR]

In the metropolitan cities of developed and developing countries, longer journeys are mostly performed by two or more modes. In the event of availability of suburban trains and public buses, commuters prefer to travel a longer stretch of their journeys by train, so as to avoid traffic congestion on roads, and the remaining part by buses to reach local areas if their final destination is not in close proximity to railway stations. Normally suburban trains have fixed corridors and buses have the flexibility to serve remote local areas. Thus design of feeder routes from railway stations to various destinations and the transfer time from trains to buses plays a very important role and can be controlled by transport planners. A considerable amount of research has been done on the independent design of a bus route network without considering the effect of train services. Researchers have made attempts using heuristics, simulation, expert systems, artificial intelligence, and optimization techniques for design of routes and schedules. So far, limited effort has been made in modeling coordinated operations. In this research, a new hybrid algorithm which exploits the benefits of genetic algorithms and a well tested heuristic algorithm for the study area is discussed. More convincing results in terms of feeder routes and coordinated schedules at the selected railway station are obtained by the proposed hybrid algorithm as compared to earlier approaches adopted by the writers for the same study area. [ABSTRACT FROM AUTHOR]

Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model. [ABSTRACT FROM AUTHOR]

The feedforward control canal problem is stated as a nonlinear optimization problem with constraints on the gate movements. It is numerically solved with the use of the sequential quadratic problem (SQP) method and the active set. The main objective of the resulting GoRoSo algorithm is the scheduling of the gate openings over a programmed operation scenario, and their calculation relies on the use of the Saint-Venant's complete model over a prediction horizon. A numerical and analytical procedure is developed to quantify the influence of any gate movement over the canal flow conditions for a prediction time horizon. All these influences are lumped together in a global matrix, which is referred to as the hydraulic influence matrix. The GoRoSo algorithm is validated by means of a series of test developed by the ASCE task committee on canal automation algorithms. [ABSTRACT FROM AUTHOR]

In this paper the authors propose a global-local methodology for optimizing the short-term operation of hydroelectric plants. The authors determine the tradeoffs between minimizing the daily release from the plant and minimizing the number of startups and shutdowns of the generating units. The model is formulated as a mixed integer, nonlinear programming optimization problem with multiple objectives. The authors consider the nonlinearities of the generating units without simplifications or approximations. The authors develop a solution method that combines an evolutionary algorithm for the global search of the integer variables and a gradient-based local optimizer for the continuous variables. The local optimizer is embedded in the global search algorithm. Convergence is achieved by iterating between the global search and the local optimizer. The proposed methodology is applied to a moderately sized Brazilian hydroelectric plant that belongs to the national interconnected system. Additionally, a comparative study was conducted using historical operational records. The results demonstrate that the proposed methodology is feasible for online daily operations and delivers two specific benefits. The first is the efficiency gained, as the model seeks to operate the generating units as close as possible to their most efficient operating points. The second benefit is reduction of the units' maintenance costs, as the model minimizes switching on/off of the generating units. [ABSTRACT FROM AUTHOR]

Completing a project with minimal time as well as minimal cost is a critical factor for scheduling a project. However, because completion speed tends to be correlated with cost (e.g., usually, time can be saved if more workers are hired), the relationship between time and cost is a trade-off. This study employed a phenomenon-mimicking algorithm, harmony search to perform this biobjective trade-off. The harmony search algorithm was applied to two test examples, and good Pareto solutions were obtained when compared with other algorithms, such as the genetic algorithm and the ant colony optimization algorithm. The harmony search algorithm explored only a small amount of total solution space in order to solve this combinatorial optimization problem. The model in this study has obtained good results with a network of up to 18 nodes, however it is anticipated that the model could be useful to solve bigger networks in practice without any difficult problem. [ABSTRACT FROM AUTHOR]

Construction contractors often finance projects using bank credit lines that allow contractors to withdraw money up to certain credit limits. Finance-based scheduling provides schedules that ensure that the contractor’s indebtedness at any time during the construction stage does not exceed the credit limit. Generally, constricted credit limits tend to yield prolonged schedules. Provided that credit limits can be adequately relaxed, compressed schedules of compressed-duration activities can be attained. Devising a compressed schedule calls for the incorporation of time-cost trade-off (TCT) analysis to strike a balance between the decreased overhead costs and the increased direct costs of the activities. Since employing TCT analysis usually causes great fluctuations in the daily resource requirements by mixing compressed-duration activities of high resource demand with others of low resource demand, therefore, the need for resource management techniques becomes inevitable to ensure efficient utilization of resources. This note used genetic algorithms to expand finance-based scheduling to devise schedules for relaxed credit limits. A prototype system was developed and coded using VISUAL BASIC, then demonstrated using a five-activity example project. The prototype was validated by comparing the results with those obtained by using the integer programming. Expanding finance-based scheduling to handle the whole spectrum of credit limits helps devise overall-optimized schedules that consider cash, time, cost, and resources. [ABSTRACT FROM AUTHOR]

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO. [ABSTRACT FROM AUTHOR]